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Share your path to resolve issues with Savvy's Chrome Extension

Hacker News

Share your path to resolve issues with Savvy's Chrome Extension

Track and Share links used to resolve issues from your browser history with Savvy's Chrome extension Try it out from the Chrome Web Store: https://chromewebstore.google.com/detail/savvy/jocphfjphhfbd... Use Cases: - Share your debug path or highlight links crucial to solving a bug. - Attach a log of your actions to any issue or postmortem. Privacy Savvy's Chrome extension does not store any of your browsing history. It reads your browsing history to surface relevant links (all done client side). Selected links can be copied to your clipboard or sent to Savvy's CLI. You can choose to store workflows generated from Savvy's CLI on Savvy or export data locally on your machine. Drop a comment if you have any questions or suggestions.

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google · Missing: agents, macos, agent
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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